Concepts Can Be Used in Real-World Applications

A video system that can automatically recognize a UMass Lowell shuttle bus. A way of making a profit trading in the stock market. A trip adviser that can recommend popular tourist spots. A blackjack buddy that can suggest the best action to take.

These are just some of the interesting class projects developed by computer science students enrolled in the Artificial Intelligence (AI) course taught by Assoc. Prof. Fred Martin.

“There are few areas in computer science as broad as artificial intelligence,” says Martin. “AI encompasses algorithm design, agents and robots, natural language understanding, expert systems and music and sound, as well as philosophical questions about the nature of consciousness.”

His students were encouraged to develop ideas theoretically and with practical programming challenges. These problem sets taught them fundamental concepts in AI, such as informed state-space search, probabilistic inference and reinforcement learning, which the students could then use in real-world applications, such as natural language processing, computer vision and robotics.

“A big part of AI is not just knowing a collection of approaches, but understanding which approach is right for which problem,” notes Martin. “To encourage the development of these skills, students were required to come up with a significant semester project where they applied ideas from the course to a problem of their own choosing.”

Students Showcase Their Ideas

Sean Cronin designed his shuttle bus video spotter as a way for a computer program to learn what the University bus looks like based on its features, and to then classify the video frames as “Bus” or “No Bus.”

“This detection ability could be used to let people know when a bus is going to arrive,” explains Cronin.

He says a webcam can be mounted a short distance from the bus stop to give riders a few minutes’ notice with their smartphones or other mobile devices.

“This way, you don’t have to freeze in winter while waiting for the bus,” he adds.

John Fallon’s program uses Hidden Markov Models to analyze the stock market and predict future prices of a given stock.

“My program used ten different stocks during the years 2009 to 2011 for the training data and 2011 to 2012 for the test data,” says Fallon. “My investment yielded a 25 percent profit.”

Mark Lubin’s “Triplicant” travel recommendation engine can recommend an optimal travel route based on given start and end points, plus a “detour factor,” which represents how much time and money one is willing to spend traveling between the end points.

“Triplicant can also identify popular locations and rank them using geotagged photo metadata extracted from Flickr,” says Lubin.

The goal of Jonathan Yu’s “Blackjack Buddy” project is to design and implement an AI agent that uses Q-Learning to play blackjack competently without knowing how to play the game.

“The agent can be used in an advisory role, suggesting optimal actions to a human player and continuously learning while the player plays,” explains Yu. “Being tasked with creating not only the AI methods, but also the blackjack game surrounding it, was both challenging and immensely more rewarding.”

Blackjack is a complex game with millions of possible combinations of dealer and player hands.

“Blackjack Buddy is able to learn an optimal strategy after approximately 5,000 simulations of the game,” he notes. “Whereas a random strategy would win 25 to 30 percent of the time, Blackjack Buddy can win anywhere from 40 to 50 percent of the games, right in line with even the most sophisticated human players.”